Systems biology

Systems biology is the computational and mathematical modeling of complex biological systems . It is a biology -based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach ( holism instead of the traditional reductionism ) to biological research.

Particularly from the year 2000 onwards, the concept has been widely used in biology in a variety of contexts. The Human Genome Project is an example of applied systems thinking in biology which has led to new, collaborative ways of working on problems in the biological field of genetics. [1] One of the AIMS of systems biology is to model and discover emergent properties , properties of cells , tissues and organisms functioning as a system Whose theoretical description is only possible using technical of systems biology. [2] These typically involve metabolic networks goldcell signaling networks. [3]

Overview

Systems biology can be considered from a number of different aspects:

  • As a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway or the heart beats ). [4] [5] [6]
  • As a paradigm , usually defined in antithesis to the so-called reductionist paradigm ( biological organization ), which is fully consistent with the scientific method . The distinction between the two paradigms is referred to in these quotations:
“The reductionist approach has successfully identified the most important features of the interactions of the environment, through quantitative measures, multiple components and simultaneous integration with mathematical models “ (Sauer et al. ). [7]
“Systems biology … it’s about putting together rather than taking apart, it’s a question of how it works, how it works. , in the full sense of the term “ ( Denis Noble ). [6]
  • As a series of operational protocols for performing research, namely a cycle composed of theory, an analytically or a computational modeling of a specific test, the computational model or theory. [8] Since the objective is a model of the interactions in a system, the experimental techniques that are as comprehensive as possible. Therefore, transcriptomics , metabolomics , proteomics and high-throughput techniquesare used to collect quantitative data for the construction and validation of models. [9]
  • As the application of dynamical systems theory to molecular biology . Indeed, the focus on the dynamics of the studied systems is the main conceptual difference between systems biology and bioinformatics . [10]
  • As a socioscientific phenomenon defined by the strategy of integration of complex data on the interactions in biological systems from various experimental sources using interdisciplinary tools and personnel. [11]

This variety of viewpoints is illustrative of the fact that systems biology refers to a cluster of peripherally overlapping concepts rather than a single well-delineated field. However, the term has widespread currency and popularity as of 2007, with terms and systems of biology proliferating worldwide.

History

Systems biology finds its roots in: citation needed ]

  • the quantitative modeling of enzyme kinetics , a discipline that flourished between 1900 and 1970,
  • the mathematical modeling of population dynamics ,
  • the simulations developed to study neurophysiology ,
  • control theory and cybernetics ,
  • synergetics .

One of the theorists who can be seen as one of the precursors of systems biology is Ludwig von Bertalanffy with his general systems theory . [12] One of the first numerical simulations in cell biology Was published in 1952 by the British neurophysiologists and Nobel prize winners Alan Lloyd Hodgkin and Andrew Fielding Huxley , Who constructed a mathematical model That Explained the Action potential Propagating along the axon of a neuron cell . [13]Their model describes a cellular function emerging from the interaction between two differentpotassium and a sodium channel , and can be considered as the beginning of computational systems biology. [14] Also in 1952, Alan Turing published The Chemical Basis of Morphogenesis , describing how non-uniformity could arise in an initially homogeneous biological system. [15]

In 1960, Denis Noble developed the first computer model of the heart pacemaker . [16]

The formal study of systems biology, a separate discipline, was launched by systems theorist Mihajlo Mesarovic in 1966 with an international symposium at the Case Institute of Technology in Cleveland , Ohio , entitled “Systems Theory and Biology”. [17] [18]

The 1960s and 1970s saw the development of several approaches to study complex molecular systems, such as the metabolic control analysis and the biochemical systems theory . The successes of molecular biology throughout the 1980s, coupled with a skepticism approach to theoretical biology , which is more likely to be achieved because of the quantitative modeling of biological processes to become a minor field. [19]

However, the birth of functional genomics in the 1990s meant that large quantities of high-quality data became available, while computing power exploded, making more realistic models possible. In 1992, then 1994, serial articles [20] [21] [22] [23] [24] On systems medicine, systems engineering, and systems biological engineering by BJ Zeng was published in China and was giving a lecture on biosystems theory and First International Conference on Transgenic Animals, Beijing, 1996. In 1997, the group of Masaru Tomita published the first quantitative model of the metabolism of a (hypothetical) cell. [25]

Around the year 2000, after Institutes of Systems Biology were established in Seattle and Tokyo , systems biology emerged as a movement in its own right, spurred on by the completion of various genome projects , the large increase in data of the omics (eg, genomics and proteomics ) and the accompanying advances in high-throughput experiments and bioinformatics .

In 2002, the National Science Foundation (NSF) put forward a major challenge for systems biology in the 21st century to build a mathematical model of the whole cell. [26] In 2003, work at the Massachusetts Institute of Technologywas initiated on CytoSolve, a method to model the whole cell by dynamically integrating multiple molecular pathway models. [27] [28] Since then, various research institutes have been developed. For example, the NIGMS of NIHestablished a project which is currently supporting the United States. [29]As of summer 2006, due to a shortage of people in systems biology [30] Several doctoral training programs in systems biology-have-been Established in Many Parts of the world. In that same year, the National Science Foundation (NSF) put forward a major challenge for systems biology in the 21st century to build a mathematical model of the whole cell. [31] In 2012 the first whole-cell model of Mycoplasma Genitalium was carried out by the Karr Laboratory at Mount Sinai School of Medicine in New York. The whole-cell model is able to predict the viability of M. Genitalium cells in response to genetic mutations. [32]

An important milestone in the development of biology systems has become the international project Physiome .

Associated disciplines

According to the interpretation of Systems Biology and the ability to obtain

  • Phenomics
Organismal variation in phenotype as it changes during its life span.
  • Genomics
Organismal deoxyribonucleic acid (DNA) sequence, including intra-organamal cell specific variation. (ie, telomere length variation)
  • Epigenomics / Epigenetics
Organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (ie, DNA methylation , Histone acetylation and deacetylation , etc.).
  • transcriptomics
Organismal, tissue or whole cell gene expression measurements by DNA microarrays or serial analysis of gene expression
  • Interferomics
Organismal, tissue, or cell-level transcript correcting factors (ie, RNA interference )
  • Proteomics
Organismal, tissue, or cell level measurements of proteins and peptides via two-dimensional gel electrophoresis , mass spectrometry or multi-dimensional protein identification techniques (advanced HPLC systems coupled with mass spectrometry ). Sub disciplines include phosphoproteomics , glycoproteomics and other methods to detect chemically modified proteins.
  • Metabolomics
Organismal, tissue, or cell-level measurements of small molecules known as metabolites
  • Glycomics
Organismal, tissue, or cell-level measurements of carbohydrates
  • Lipidomics
Organismal, tissue, or cell level measurements of lipids .

In addition to the identification and quantification of the above-mentioned molecules, further analysis of the dynamics and interactions within a cell. This includes: citation needed ]

  • Interactomics
Organismal, tissue, or cell level study of interactions between molecules. Currently, the authoritative molecular discipline in this field of study is protein-protein interactions (PPI), with the working definition does not preclude inclusion of other molecular disciplines such as those defined here.
  • NeuroElectroDynamics
Organismal, brain computing function, dynamic biophysical mechanisms and emerging computation by electrical interactions.
  • Fluxomics
Organismal, tissue, or cell level measurements of molecular dynamic changes over time.
  • Biomics
Systems analysis of the biome .
  • Molecular Biokinematics
The study of “biology in motion”. Various technologies used to capture dynamic changes in mRNA, proteins, and post-translational modifications.
  • Semiomics
Analysis of the system of sign relationships of an organism or other biosystems.
  • Physiomics
A systematic study of physiology in biology.

Cancer Systems Biology is an example of the systems biology approach, which can be distinguished by the specific object of study ( tumorigenesis and treatment of cancer ). It works with the specific data (patient samples, high-throughput data with particular attention to characterizing cancer genome in patient tumor samples) and tools (immortalized cancer cell lines , mouse models of tumorigenesis, xenograft models, Next Generation Sequencingmethods, siRNA-based gene knocking down screenings , computational modeling of the consequences of somatic mutations and genome instability). [33] The long-term objective of the systems biology of cancer is Ability to better diagnose cancer, classify it and better predict the outcome of a suggéré treatment, qui est a basis for personalized cancer medicine and virtual cancer patient in more remote prospective. Significant Efforts in Computational Biology of Cancer Systems have been made in realistic multi-scale in silico models of various tumors. [34]

The investigations are frequently combined with large-scale perturbation methods, including gene-based ( RNAi , mis-expression of wild type and mutant genes) and chemical approaches using small molecule libraries. citation needed ] Robots and automated sensors enable such large-scale experimentation and data acquisition. These technologies are still emerging and many face problems, the lower the quality. citation needed ] A wide variety of quantitative scientists ( computational biologists , statisticians , mathematicians , computer scientistsand physicists ) are working to improve the quality of these approaches and to create, refine, and retest the models.

The systems biology approach often involves the development of mechanistic models , such as the reconstruction of dynamic systems of the quantitative properties of their elementary building blocks. [35] [36] [37] [38] For instance, a cellular network can be modeled mathematically using methods from chemical kinetics and control theory . Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used (eg, flow balance analysis ). [37]

Bioinformatics and data analysis

Other aspects of computer science, informatics , and statistics sont également used in systems biology. These include:

  • New forms of computational models, Such As the use of process calculi to model biological processes (notable approaches include stochastic π-calculus , BioAmbients, Beta Binders, BioPEPA, and Brane calculus) and constraint -based modeling.
  • Integration of information from the literature, using techniques of information mining and text mining . [39]
  • Development of online databases and repositories for sharing data and models, approaches to database integration and software interoperability via loose coupling of software, websites and databases, or commercial suits.
  • Development of syntactically and semantically sound ways of living biological models. quote needed ]
  • Network-based approaches for analyzing high dimensional genomic data sets. For example, weighted correlation network analysis is often used for identifying clusters (referred to as modules), modeling the relationship between clusters, calculating fuzzy measures of cluster (module) membership, identifying intramodular hubs, and clustering for clustering in other data sets.
  • Pathway-based methods for data analysis, eg, approaches to identify and score pathways with differential activity of their gene, protein, or metabolite members. [40]

See also

  • Biological computation
  • Computational biology
  • interactome
  • exposome
  • Network Biology
  • Weighted correlation network analysis
  • Synthetic biology
  • List of topics in biology
  • Systems biologists
  • Biomedicine Systems
  • Flow balance analysis
  • Metabolic network modeling
  • Molecular pathological epidemiology
  • Systems pharmacology
  • Cancer systems biology
  • Network medicine

References

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